Relaxation Acupressure Reduces Persistent Cancer-Related Fatigue

Persistent cancer-related fatigue (PCRF) is a symptom experienced by many cancer survivors. Acupressure offers a potential treatment for PCRF. We investigated if acupressure treatments with opposing actions would result in differential effects on fatigue and examined the effect of different “doses” of acupressure on fatigue. We performed a trial of acupressure in cancer survivors experiencing moderate to severe PCRF. Participants were randomized to one of three treatment groups: relaxation acupressure (RA), high-dose stimulatory acupressure (HIS), and low-dose stimulatory acupressure (LIS). Participants performed acupressure for 12-weeks. Change in fatigue as measured by the Brief Fatigue Inventory (BFI) was our primary outcome. Secondary outcomes were assessment of blinding and compliance to treatment. Fatigue was significantly reduced across all treatment groups (mean ± SD reduction in BFI: RA 4.0 ± 1.5, HIS 2.2 ± 1.6, LIS 2.7 ± 2.2), with significantly greater reductions in the RA group. In an adjusted analysis, RA resulted in significantly less fatigue after controlling for age, cancer type, cancer stage, and cancer treatments. Self-administered RA caused greater reductions in fatigue compared to either HIS or LIS. The magnitude of the reduction in fatigue was clinically relevant and could represent a viable alternative for cancer survivors with PCRF

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as novel risk factors for Alzheimers Disease

The genetic component of Alzheimer’s disease (AD) has been mainly assessed using Genome Wide Association Studies (GWAS), which do not capture the risk contributed by rare variants. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals —16,036 AD cases and 16,522 controls— in a two-stage analysis. Next to known genes TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Next to these genes, the rare variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential driver genes in AD-GWAS loci. Rare damaging variants in these genes, and in particular loss-of-function variants, have a large effect on AD-risk, and they are enriched in early onset AD cases. The newly identified AD-associated genes provide additional evidence for a major role for APP-processing, Aβ-aggregation, lipid metabolism and microglial function in AD

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease

Alzheimer’s disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals—16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD

THE CRITICAL ROLE OF MARKETS IN CLIMATE CHANGE ADAPTATION

Markets, especially land markets, can facilitate climate change adaptation through price signals. A review of research reveals that urban, coastal, and agricultural land markets provide effective signals of the emerging costs of climate change. These signals encourage adjustments by both private owners and policy officials in taking preemptive action to reduce costs. In agriculture, they promote consideration of new cropping and tillage practices, seed types, timing, and location of production. They also stimulate use of new irrigation technologies. In urban areas, they motivate new housing construction, elevation, and location away from harm. They channel more efficient use of water and its application to parks and other green areas to make urban settings more desirable with higher temperatures. Related water markets play a similar role in adjusting water use and reallocation. To be effective, however, markets must reflect multiple traders and prices must be free to adjust. Where these conditions are not met, market signals will be inhibited and market-driven adaptation will be reduced. Because public policy is driven by constituent demands, it may not be a remedy. The evidence of the National Flood Insurance Program and federal wildfire response illustrates how politically difficult it may be to adjust programs to be more adaptive

THE CRITICAL ROLE OF MARKETS IN CLIMATE CHANGE ADAPTATION

Markets, especially land markets, can facilitate climate change adaptation through price signals. A review of research reveals that urban, coastal, and agricultural land markets provide effective signals of the emerging costs of climate change. These signals encourage adjustments by both private owners and policy officials in taking preemptive action to reduce costs. In agriculture, they promote consideration of new cropping and tillage practices, seed types, timing, and location of production. They also stimulate use of new irrigation technologies. In urban areas, they motivate new housing construction, elevation, and location away from harm. They channel more efficient use of water and its application to parks and other green areas to make urban settings more desirable with higher temperatures. Related water markets play a similar role in adjusting water use and reallocation. To be effective, however, markets must reflect multiple traders and prices must be free to adjust. Where these conditions are not met, market signals will be inhibited and market-driven adaptation will be reduced. Because public policy is driven by constituent demands, it may not be a remedy. The evidence of the National Flood Insurance Program and federal wildfire response illustrates how politically difficult it may be to adjust programs to be more adaptive